Aeroelastic Tailoring of Composite Wing Design Using Bee Colony Optimisation

2014 ◽  
Vol 629 ◽  
pp. 182-188 ◽  
Author(s):  
Tan Kai Jun ◽  
Mohammad Yazdi Harmin ◽  
Fairuz I. Romli
Keyword(s):  
Fibre Orientation ◽  
Statistical Investigation ◽  
Computational Time ◽  
Test Cases ◽  
Wing Design ◽  
Aeroelastic Tailoring ◽  
Bee Colony ◽  
Made In ◽  
Composite Wing

Bee Colony Optimisation (BCO) method is used to optimise the fibre orientation of a simple rectangular composite wing with respect to maximising flutter/divergence speed. A modified implementation is proposed to provide a suitable version of BCO algorithm for solving the multi-variable optimisation problem. 50 test cases are performed and the statistical investigation is made in order to investigate the effectiveness and robustness of the proposed algorithm. Consideration is also made in terms of the best weightage of the minimum confident parameter. The overall results indicate that the modified BCO algorithm offers outstanding performance in terms of both accuracy and computational time.

scholarly journals Efficient Method for Aeroelastic Tailoring of Composite Wing to Minimize Gust Response

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Yu ◽  
Zhengjie Wang ◽  
Shijun Guo
Keyword(s):  
Dynamic Response ◽  
Efficient Method ◽  
Laminated Composite ◽  
Optimization Process ◽  
Computational Time ◽  
Response Analysis ◽  
Dynamic Response Analysis ◽  
Aeroelastic Tailoring ◽  
Gust Response ◽  
Composite Wing

Aeroelastic tailoring of laminated composite structure demands relatively high computational time especially for dynamic problem. This paper presents an efficient method for aeroelastic dynamic response analysis with significantly reduced computational time. In this method, a relationship is established between the maximum aeroelastic response and quasi-steady deflection of a wing subject to a dynamic loading. Based on this relationship, the time consuming dynamic response can be approximated by a quasi-steady deflection analysis in a large proportion of the optimization process. This method has been applied to the aeroelastic tailoring of a composite wing of a tailless aircraft for minimum gust response. The results have shown that 20%–36% gust response reduction has been achieved for this case. The computational time of the optimization process has been reduced by 90% at the cost of accuracy reduction of 2~4% comparing with the traditional dynamic response analysis.

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

Performance Study of Harmony Search Algorithm for Multilevel Thresholding

2016 ◽  
Vol 25 (4) ◽  
pp. 473-513 ◽  
Author(s):  
Salima Ouadfel ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Performance Study ◽  
Bacterial Foraging Optimization ◽  
Bee Colony ◽  
Fitness Value ◽  
Multiple Thresholds

AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.

Influence of weight modelling on the outcome of wing design using multidisciplinary design optimisation techniques

2013 ◽  
Vol 117 (1195) ◽  
pp. 871-895 ◽  
Author(s):  
J. Mariens ◽  
A. Elham ◽  
M. J. L. van Tooren
Keyword(s):  
Estimation Method ◽  
Lower Class ◽  
Multidisciplinary Design ◽  
Estimation Methods ◽  
Computational Time ◽  
Weight Estimation ◽  
Wing Design ◽  
Design Optimisation ◽  
Multidisciplinary Design Optimisation ◽  
Optimisation Techniques

Abstract Weight estimation methods are categorised in different classes based on their level of fidelity. The lower class methods are based on statistical data, while higher class methods use physics based calculations. Statistical weight estimation methods are usually utilised in early design stages when the knowledge of designers about the new aircraft is limited. Higher class methods are applied in later design steps when the design is mature enough. Lower class methods are sometimes preferred in later design stages, even though the designers have enough knowledge about the design to use higher class methods. In high level multidisciplinary design optimisation (MDO) fidelity is often sacrificed to obtain models with shorter computation times. There is always a compromise required to select the proper weight estimation method for an MDO project. An investigation has been performed to study the effect of using different weight estimation methods, with low and medium levels of fidelity, on the results of a wing design using multidisciplinary design optimisation techniques. An MDO problem was formulated to design the wing planform of a typical turboprop and a turbofan passenger aircraft. The aircraft maximum take-off weight was selected as the objective function. A quasi-three-dimensional aerodynamic solver was developed to calculate the wing aerodynamic characteristics. Five various statistical methods and a quasi-analytical method are used to estimate the wing structural weight. These methods are compared to each other by analysing their accuracy and sensitivity to different design variables. The results of the optimisations showed that the optimum wing shape is affected by the method used to estimate the wing weight. Using different weight estimation methods also strongly affects the optimisation convergence history and computational time.

Effect of Aeroelastic Tailoring of Composite Wing Skin Laminates on Controllability of Aircraft

2021 ◽  
Author(s):  
Varakini Sanmugadas ◽  
Rikin Gupta ◽  
Wei Zhao ◽  
Rakesh K. Kapania ◽  
David K. Schmidt
Keyword(s):  
Aeroelastic Tailoring ◽  
Wing Skin ◽  
Composite Wing

scholarly journals Comparison Between SMILES-Based Differential Neural Computer and Recurrent Neural Network Architectures for De Novo Molecule Design

2019 ◽  
Author(s):  
Simon Johansson ◽  
Oleksii Ptykhodko ◽  
Josep Arús-Pous ◽  
Ola Engkvist ◽  
Hongming Chen
Keyword(s):  
De Novo ◽  
Molecular Design ◽  
Research Area ◽  
Computational Time ◽  
Test Cases ◽  
Sequential Data ◽  
Network Architectures ◽  
De Novo Molecular Design ◽  
Molecule Design ◽  
The Cost

In recent years, deep learning for de novo molecular generation has become a rapidly growing research area. Recurrent neural networks (RNN) using the SMILES molecular representation is one of the most common approaches used. Recent study shows that the differentiable neural computer (DNC) can make considerable improvement over the RNN for modeling of sequential data. In the current study, DNC has been implemented as an extension to REINVENT, an RNN-based model that has already been used successfully to make de novo molecular design. The model was benchmarked on its capacity to learn the SMILES language on the GDB-13 and MOSES datasets. The DNC shows improvement on all test cases conducted at the cost of significantly increased computational time and memory consumption.

scholarly journals An Adaptive Embedding Strength Watermarking Algorithm Based on Shearlets’ Capture Directional Features

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1377
Author(s):  
Qiumei Zheng ◽  
Nan Liu ◽  
Fenghua Wang
Keyword(s):  
Artificial Bee Colony ◽  
Discrete Wavelet ◽  
Geometric Attacks ◽  
Bee Colony ◽  
Adaptive Embedding ◽  
Strong Robustness ◽  
Simulation Results ◽  
Fixed Embedding ◽  
False Positive Problem ◽  
Made In

The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity.

scholarly journals Effective PCB Decoupling Optimization by Combining an Iterative Genetic Algorithm and Machine Learning

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1243 ◽  
Author(s):  
Riccardo Cecchetti ◽  
Francesco de Paulis ◽  
Carlo Olivieri ◽  
Antonio Orlandi ◽  
Markus Buecker
Keyword(s):  
Genetic Algorithm ◽  
Power Delivery ◽  
Computational Time ◽  
Test Cases ◽  
Experimental Measurements ◽  
New Approach ◽  
Power Delivery Network ◽  
Capacitor Placement ◽  
Minimum Number ◽  
Artificial Neural Network Ann

An iterative optimization for decoupling capacitor placement on a power delivery network (PDN) is presented based on Genetic Algorithm (GA) and Artificial Neural Network (ANN). The ANN is first trained by an appropriate set of results obtained by a commercial simulator. Once the ANN is ready, it is used within an iterative GA process to place a minimum number of decoupling capacitors for minimizing the differences between the input impedance at one or more location, and the required target impedance. The combined GA–ANN process is shown to effectively provide results consistent with those obtained by a longer optimization based on commercial simulators. With the new approach the accuracy of the results remains at the same level, but the computational time is reduced by at least 30 times. Two test cases have been considered for validating the proposed approach, with the second one also being compared by experimental measurements.

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